• Advances in Computational Design
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• v.7 no.1
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• pp.1-17
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• 2022

In this study, a new strategy is presented to transmit the fundamental elastic beam problem into the modern optimization platform and solve it by using artificial intelligence (AI) tools. As a practical example, deflection of Euler-Bernoulli beam is mathematically formulated by 2nd-order ordinary differential equations (ODEs) in accordance to the classical beam theory. This fundamental engineer problem is then transmitted from classic formulation to its artificial-intelligence presentation where the behavior of the beam is simulated by using neural networks (NNs). The supervised training strategy is employed in the developed NNs implemented in the heuristic optimization algorithms as the fitness function. Different evolutionary optimization tools such as genetic algorithm (GA) and particle swarm optimization (PSO) are used to solve this non-linear optimization problem. The step-by-step procedure of the proposed method is presented in the form of a practical flowchart. The results indicate that the proposed method of using AI toolsin solving beam ODEs can efficiently lead to accurate solutions with low computational costs, and should prove useful to solve more complex practical applications.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.827-832
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• 2001

A multi-level optimum design algorithm for prestressed concrete (PSC) box girders is proposed in this paper. To save the numerical efforts, a multi-level optimization technique using model coordination method that separately utilizes both tendon profile design and section design is incorporated. And also, a reduced basis technique for the efficient tendon profile optimization is proposed in this paper. From the numerical investigations, it may be positively stated that the optimum design of PSC box girder based on the new approach proposed in this study will lead to more rational and economical design compared with the currently available designs.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.836-840
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• 2006

Design optimization of layered plates bonding process is conducted to achieve high product quality by considering uncertainties in a manufacturing process. During the cooling process of the sequential sub-processes, different thermal expansion coefficients lead to residual stress and displacement. thus resulting in defects on the surface of the adherent. So robust process optimization is performed to minimize the residual stress mean and variation of the assembly while constraining the distortion as well as the instantaneous maximum stress to the allowable limits. In robust process optimization, the dimension reduction (DR) method is employed to quantify both reliability and quality of the layered plate bonding. Using this method. the average and standard deviation is estimated. Response surface is constructed using the statistical data obtained by the DRM for robust objectives and constraints. from which the optimum solution is obtained.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.623-624
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• 2008

We present a level set method for numerical shape optimization of electromagnetic systems. The level set method does not only lead to efficient computational schemes, but also is able to handle topological changes such as merging, splitting and even disappearing of connected components. The velocity field on boundaries is obtained by a shape derivative of continuum sensitivity analysis using the material derivative concept and an adjoint variable technique. Two numerical results of dielectric optimization between electrodes showed that the level set method is feasible and effective in solving shape optimization problems of electromagnetic systems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.255-262
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• 2002

In this paper, an improved multi-objective optimmum design method is proposed. And it is applied to steel frames under seismic loads. The multi-objective optimization problem is formulated with three optimality criteria, namely, minimum structural weight and maximum strain energy and stability. The Pareto curve can be obtained by performing the multi-objective optimization for multistory shear buildings. In order to efficiently solve the multi-objective optimization problem the decomposition method that separates both system-level and element-level is used. In addition, various techniques such as effective reanalysis technique with respect to intermediate variables and sensitivity analysis using an automatic differentiation (AD) we incorporated. Moreover, the relationship function among section properties induced from the profile is used in order to link system-level and element level. From the results of numerical investigation, it may be stated that the proposed method will lead to the more rational design compared with the conventional one.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.390-398
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• 2014

The systematic optimal tuning of power system stabilizers (PSSs) using the dynamic embedded optimization (DEO) technique is described in this paper. A hybrid system model which has the differential-algebraic-impulsive-switched (DAIS) structure is used as a tool for the DEO of PSSs. Two numerical optimization methods, which are the steepest descent and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms, are investigated to implement the DEO using the hybrid system model. As well as the gain and time constant of phase lead compensator, the output limits of PSSs with non-smooth nonlinearities are considered as the parameters to be optimized by the DEO. The simulation results show the effectiveness and robustness of the PSSs tuned by the proposed DEO technique on the IEEE 39 bus New England system to mitigate system damping.

• Journal of Korean Association for Spatial Structures
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• v.21 no.4
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• pp.39-48
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• 2021

In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.29-38
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• 2023

Recent studies have highlighted the link between diseases and inflammation across our lifespan. Our sedentary lifestyle, high-calorie diet, chronic stress, chronic infections, and exposure to pollutants and xenobiotics, collectively intensify the course and recurrence of infections and inflammation in our bodies, promoting the prevalence of chronic diseases and aging. Given such phenomena and considering additional factors such as the frequency of prescription, and easy access to over-the-counter drugs, the need for anti-inflammatory therapeutics is ever-increasing. However, the readily available anti-inflammatory treatment option comes with a greater risk of side effects or high cost (biologics). Therefore in this growing competition of discovering and developing new potent anti-inflammatory drugs, we focused on utilizing the established knowledge of traditional medicine to find lead compounds. Since lead optimization is an indispensable step toward drug development, we applied this concept for the production of potent anti-inflammatory compounds achieved by structural modification of flavonoids. The derivative obtained through acetylation of myricetin, 3,3',4',5,5',7-hexaacetate myricetin, showed a greater inhibitory effect in the production of pro-inflammatory mediators such as nitric oxide, Prostaglandin E₂, and pro-inflammatory cytokines like interleukin-6, interleukin1β, in lipopolysaccharide-stimulated RAW264.7 mouse macrophage cells compared to myricetin. The increased potency of inhibition was in conjunction with an increased inhibitory effect on inducible nitric oxide synthase and cyclooxygenase-2 proteins. Through such measures, this study supports lead optimization for well-established lead compounds from traditional medicine using a simpler and greener chemistry approach for the purpose of designing and developing potent anti-inflammatory therapeutics with possibly fewer side effects and increased bioavailability.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.905-908
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• 2009

In the management of groundwater in coastal areas, saltwater intrusion associated with extensive groundwater pumping, is an important problem. The groundwater optimization model is an advanced method to study the aquifer and decide the optimal pumping rates or optimal well locations. Cheng and Park gave the analytical solutions to the optimization problems basing on Strack's analytical solution. However, the analytical solutions have some limitations of the property of aquifer, boundary conditions, and so on. A simulation-optimization numerical method presented in this study can deal with non-homogenous aquifers and various complex boundary conditions. This simulation-optimization model includes the sharp interface solution which solves the same governing equation with Strack's analytical solution, therefore, the freshwater head and saltwater thickness should be in the same conditions, that can lead to the comparable results in optimal pumping rates and optimal well locations for both of the solutions. It is noticed that the analytical solutions can only be applied on the infinite domain aquifer, while it is impossible to get a numerical model with infinite domain. To compare the numerical model with the analytical solutions, calculation of the equivalent boundary flux was planted into the numerical model so that the numerical model can have the same conditions in steady state with analytical solutions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.321-326
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• 2007

The joined-wing is a new concept of the airplane wing. The fore-wing and the aft-wing arc joined together in the joined-wing. The range and loiter are longer than those of a conventional wing. The joined-wing can lead to increased aerodynamic performances and reduction of the structural weight. The structural behavior of the joined-wing has a high geometric nonlinearity according to the external loads. The gust loads are the most critical loading conditions in the structural design of the joined-wing. The nonlinear behavior should be considered in the optimization of the joined-wing. It is well known that conventional nonlinear response optimization is extremely expensive: therefore, the conventional method is almost impossible to use in large scale structures such as the joined-wing. In this research, geometric nonlinear response structural optimization is carried out using equivalent loads. Equivalent loads are the load sets which generate the same response field in linear analysis as that from nonlinear analysis. In the equivalent loads method, the external loads are transformed to the equivalent loads (EL) for linear static analysis, and linear response optimization is carried out based on the EL.